Helsinki Winter School
Old Members’ Trust Graduate Conference and Academic Travel Grant – Alistair J Sterling
The 34th installment of the Helsinki Winter School in Theoretical Chemistry had the topic of “machine learning” at its core. The application of machine learning in the physical sciences has recently become incredibly popular, with the development of faster and more efficient ways of storing and handling data. Previously unfathomable solutions to some of the most challenging questions in computational chemistry now have the opportunity to be attempted.
The opportunity to learn how to implement these new approaches is invaluable for my own research. A calculation of the structure of large organic molecules for the study of their physical properties would take weeks using conventional quantum chemical methods. This task can now be done in minutes.
A Winter School such as this provides a relaxed and diverse environment in which to have discussions with leaders in the field. Lectures from Michele Ceriotti, Olexander Isayev and Anatole von Lilienfeld provided great insight into how to construct machine learning calculations, and the theory necessary to understand how to implement their methods in my own research.
I would like to take this opportunity to thank the Old Members’ Trust for sponsoring my trip to Helsinki, and for giving me the opportunity to learn about machine learning for chemistry at such a prestigious event.
Published: 22 January 2019
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